Cohen et al_CLIA CRC_Clinical analysis 07012024
library(swimplot) library(coxphf) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(ComplexHeatmap) library(tidyverse) library(readxl) library(survival) library(janitor) library(openxlsx) library(writexl) library(rms) library(DT)
#ctDNA Detection rate by Stage and Window
#MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.Surveillance %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Anytime post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.anytime %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.anytime == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.anytime, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.anytime == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)#ctDNA MRD Detection rate Stage I/II vs III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"))
circ_data$Stage_Grouped <- factor(ifelse(circ_data$Stage %in% c("I", "II"), "I/II", "III"))
contingency_table <- table(circ_data$Stage_Grouped, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(contingency_table)
NEGATIVE POSITIVE
I/II 162 15
III 172 56print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 16.741, df = 1, p-value = 4.285e-05#Demographics Table
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data_subset <- circ_data %>%
select(
Age,
Gender,
PrimSite,
pT,
pN,
Stage,
Grade,
NAC,
ACT,
MSI,
BRAF.V600E,
RAS,
DFS.Event,
OS.months) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon")),
pT = factor(pT, levels = c("T0","T1-T2", "T3-T4")),
pN = factor(pN, levels = c("N0", "N1-N2")),
Stage = factor(Stage, levels = c("I","II", "III")),
Grade = factor(Grade, levels = c("G1", "G2", "G3","GX")),
NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
MSI = factor(MSI, levels = c("MSS", "MSI-High")),
BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF WT", "BRAF V600E")),
RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS WT", "RAS Mut")),
DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 7951 |
|---|---|
| Age | 61 (13 - 91) |
| Gender | |
| Male | 407 (51%) |
| Female | 388 (49%) |
| PrimSite | |
| Right-sided colon | 411 (52%) |
| Left-sided colon | 384 (48%) |
| pT | |
| T0 | 3 (0.4%) |
| T1-T2 | 133 (17%) |
| T3-T4 | 654 (83%) |
| Unknown | 5 |
| pN | |
| N0 | 310 (39%) |
| N1-N2 | 482 (61%) |
| Unknown | 3 |
| Stage | |
| I | 47 (5.9%) |
| II | 262 (33%) |
| III | 486 (61%) |
| Grade | |
| G1 | 84 (11%) |
| G2 | 559 (72%) |
| G3 | 127 (16%) |
| GX | 4 (0.5%) |
| Unknown | 21 |
| NAC | |
| Neoadjuvant Chemotherapy | 0 (0%) |
| Upfront Surgery | 795 (100%) |
| ACT | |
| Adjuvant Chemotherapy | 522 (66%) |
| Observation | 273 (34%) |
| MSI | |
| MSS | 664 (84%) |
| MSI-High | 131 (16%) |
| BRAF.V600E | |
| BRAF WT | 699 (88%) |
| BRAF V600E | 96 (12%) |
| RAS | |
| RAS WT | 459 (58%) |
| RAS Mut | 336 (42%) |
| DFS.Event | |
| Recurrence | 141 (18%) |
| No Recurrence | 654 (82%) |
| OS.months | 27 (0 - 103) |
| 1 Median (Range); n (%) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE,
strip_md_bold = TRUE)Warning: The `strip_md_bold` argument of `as_flex_table()` is deprecated as of gtsummary 1.6.0.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.fit1Characteristic | N = 7951 |
|---|---|
Age | 61 (13 - 91) |
Gender | |
Male | 407 (51%) |
Female | 388 (49%) |
PrimSite | |
Right-sided colon | 411 (52%) |
Left-sided colon | 384 (48%) |
pT | |
T0 | 3 (0.4%) |
T1-T2 | 133 (17%) |
T3-T4 | 654 (83%) |
Unknown | 5 |
pN | |
N0 | 310 (39%) |
N1-N2 | 482 (61%) |
Unknown | 3 |
Stage | |
I | 47 (5.9%) |
II | 262 (33%) |
III | 486 (61%) |
Grade | |
G1 | 84 (11%) |
G2 | 559 (72%) |
G3 | 127 (16%) |
GX | 4 (0.5%) |
Unknown | 21 |
NAC | |
Neoadjuvant Chemotherapy | 0 (0%) |
Upfront Surgery | 795 (100%) |
ACT | |
Adjuvant Chemotherapy | 522 (66%) |
Observation | 273 (34%) |
MSI | |
MSS | 664 (84%) |
MSI-High | 131 (16%) |
BRAF.V600E | |
BRAF WT | 699 (88%) |
BRAF V600E | 96 (12%) |
RAS | |
RAS WT | 459 (58%) |
RAS Mut | 336 (42%) |
DFS.Event | |
Recurrence | 141 (18%) |
No Recurrence | 654 (82%) |
OS.months | 27 (0 - 103) |
1Median (Range); n (%) | |
save_as_docx(fit1, path= "~/Downloads/table1.docx")#Heatmap with Clinical & Genomics Factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)
ha <- HeatmapAnnotation(
Stage = circ_data$Stage,
Gender = circ_data$Gender,
PrimSite = circ_data$PrimSite,
pT = circ_data$pT,
pN = circ_data$pN,
Grade = circ_data$Grade,
ACT = circ_data$ACT,
MSI = circ_data$MSI,
BRAF.V600E = circ_data$BRAF.V600E,
RAS = circ_data$RAS,
ctDNA.MRD = circ_data$ctDNA.MRD,
ctDNA.Surveillance = circ_data$ctDNA.Surveillance,
ctDNA.anytime = circ_data$ctDNA.anytime,
DFS.Event = circ_data$DFS.Event,
col = list(Stage = c("I" = "seagreen1", "II" = "orange", "III" = "purple"),
Gender = c("Female" = "goldenrod" , "Male" = "blue4"),
PrimSite = c("Right-sided colon" = "brown", "Left-sided colon" ="darkgreen"),
pT = c("T0" = "khaki","T1-T2" = "khaki", "T3-T4" ="brown2"),
pN = c("N0" = "cornflowerblue", "N1-N2" ="orange2"),
Grade = c("GX" = "grey","G1" = "coral", "G2" ="darkgreen", "G3" = "yellow3"),
ACT = c("TRUE" = "#C1211A", "FALSE" ="#008BCE"),
MSI = c("MSS" = "grey", "MSI-High" ="black"),
BRAF.V600E = c("WT" = "grey", "MUT" ="black"),
RAS = c("WT" = "grey", "MUT" ="black"),
ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.Surveillance = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.anytime = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
DFS.Event = c("TRUE" = "red3", "FALSE" ="blue")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()#DFS by ctDNA at the MRD Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 328 29 NA NA NA
ctDNA.MRD=POSITIVE 62 36 11.3 8.67 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 110.000 25.000 0.903 0.019 0.859 0.935
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 12.0000 36.0000 0.3608 0.0669 0.2332 0.4898 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.2879 9.8539 0.2517 9.088 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.854 0.1015 6.016 16.14
Concordance= 0.742 (se = 0.029 )
Likelihood ratio test= 74.36 on 1 df, p=<2e-16
Wald test = 82.6 on 1 df, p=<2e-16
Score (logrank) test = 124 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.85 (6.02-16.14); p = 0"#Multivariate cox regression for DFS at the MRD Window & Age threshold as 50 years - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group2 + PrimSite + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#Univariate cox regression for factors used in MVA - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male")) #univariate for gender
cox_fit <- coxph(surv_object ~ Gender, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ Gender, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
GenderMale 0.3259 1.3853 0.2550 1.278 0.201
exp(coef) exp(-coef) lower .95 upper .95
GenderMale 1.385 0.7219 0.8404 2.284
Concordance= 0.535 (se = 0.032 )
Likelihood ratio test= 1.67 on 1 df, p=0.2
Wald test = 1.63 on 1 df, p=0.2
Score (logrank) test = 1.65 on 1 df, p=0.2cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.39 (0.84-2.28); p = 0.201"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50")) #univariate for Age
cox_fit <- coxph(surv_object ~ Age.Group2, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ Age.Group2, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
Age.Group2≥50 0.1934 1.2133 0.3102 0.623 0.533
exp(coef) exp(-coef) lower .95 upper .95
Age.Group2≥50 1.213 0.8242 0.6606 2.229
Concordance= 0.526 (se = 0.024 )
Likelihood ratio test= 0.4 on 1 df, p=0.5
Wald test = 0.39 on 1 df, p=0.5
Score (logrank) test = 0.39 on 1 df, p=0.5cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.21 (0.66-2.23); p = 0.533"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon")) #univariate for Tumor Location
cox_fit <- coxph(surv_object ~ PrimSite, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ PrimSite, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
PrimSiteLeft-sided colon 0.2250 1.2523 0.2482 0.907 0.365
exp(coef) exp(-coef) lower .95 upper .95
PrimSiteLeft-sided colon 1.252 0.7985 0.77 2.037
Concordance= 0.518 (se = 0.032 )
Likelihood ratio test= 0.82 on 1 df, p=0.4
Wald test = 0.82 on 1 df, p=0.4
Score (logrank) test = 0.83 on 1 df, p=0.4cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.25 (0.77-2.04); p = 0.365"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4")) #univariate for Overall T Stage
cox_fit <- coxph(surv_object ~ pT, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ pT, data = circ_data)
n= 386, number of events= 65
(4 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pTT3-T4 1.0960 2.9923 0.5163 2.123 0.0338 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pTT3-T4 2.992 0.3342 1.088 8.231
Concordance= 0.549 (se = 0.017 )
Likelihood ratio test= 6.26 on 1 df, p=0.01
Wald test = 4.51 on 1 df, p=0.03
Score (logrank) test = 4.98 on 1 df, p=0.03cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.99 (1.09-8.23); p = 0.034"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2")) #univariate for Overall N Stage
cox_fit <- coxph(surv_object ~ pN, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ pN, data = circ_data)
n= 388, number of events= 65
(2 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pNN1-N2 1.6966 5.4556 0.3592 4.723 2.32e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pNN1-N2 5.456 0.1833 2.698 11.03
Concordance= 0.667 (se = 0.023 )
Likelihood ratio test= 31.89 on 1 df, p=2e-08
Wald test = 22.31 on 1 df, p=2e-06
Score (logrank) test = 28.18 on 1 df, p=1e-07cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.46 (2.7-11.03); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High")) #univariate for MSI
cox_fit <- coxph(surv_object ~ MSI, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ MSI, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
MSIMSI-High 0.2018 1.2236 0.3312 0.609 0.542
exp(coef) exp(-coef) lower .95 upper .95
MSIMSI-High 1.224 0.8173 0.6393 2.342
Concordance= 0.531 (se = 0.028 )
Likelihood ratio test= 0.35 on 1 df, p=0.6
Wald test = 0.37 on 1 df, p=0.5
Score (logrank) test = 0.37 on 1 df, p=0.5cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.22 (0.64-2.34); p = 0.542"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E")) #univariate for BRAF
cox_fit <- coxph(surv_object ~ BRAF.V600E, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ BRAF.V600E, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
BRAF.V600EV600E 0.5464 1.7271 0.3199 1.708 0.0876 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
BRAF.V600EV600E 1.727 0.579 0.9226 3.233
Concordance= 0.552 (se = 0.028 )
Likelihood ratio test= 2.59 on 1 df, p=0.1
Wald test = 2.92 on 1 df, p=0.09
Score (logrank) test = 2.99 on 1 df, p=0.08cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.73 (0.92-3.23); p = 0.088"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut")) #univariate for RAS
cox_fit <- coxph(surv_object ~ RAS, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ RAS, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
RASMut 0.1691 1.1843 0.2507 0.675 0.5
exp(coef) exp(-coef) lower .95 upper .95
RASMut 1.184 0.8444 0.7246 1.936
Concordance= 0.511 (se = 0.031 )
Likelihood ratio test= 0.45 on 1 df, p=0.5
Wald test = 0.46 on 1 df, p=0.5
Score (logrank) test = 0.46 on 1 df, p=0.5cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.18 (0.72-1.94); p = 0.5"#DFS by ctDNA at the MRD Window - Stage II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 141 5 NA NA NA
ctDNA.MRD=POSITIVE 10 3 NA 11.1 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 46.000 5.000 0.949 0.023 0.878 0.979
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 3.000 0.700 0.145 0.329 0.892 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 151, number of events= 8
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.1194 8.3262 0.7317 2.897 0.00377 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 8.326 0.1201 1.985 34.93
Concordance= 0.68 (se = 0.09 )
Likelihood ratio test= 6.3 on 1 df, p=0.01
Wald test = 8.39 on 1 df, p=0.004
Score (logrank) test = 12.02 on 1 df, p=5e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 8.33 (1.98-34.93); p = 0.004"#DFS by ctDNA at the MRD Window - Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 170 24 NA NA NA
ctDNA.MRD=POSITIVE 51 33 10 7.13 16.2event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 57.0000 20.0000 0.8582 0.0303 0.7863 0.9073
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 6.0000 33.0000 0.2704 0.0714 0.1433 0.4146 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 221, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.0479 7.7515 0.2728 7.506 6.08e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 7.752 0.129 4.541 13.23
Concordance= 0.731 (se = 0.031 )
Likelihood ratio test= 53.81 on 1 df, p=2e-13
Wald test = 56.35 on 1 df, p=6e-14
Score (logrank) test = 77.35 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.75 (4.54-13.23); p = 0"#DFS by ctDNA at the MRD Window - Stage II & Risk Groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$Risk.Group!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.Risk = case_when(
ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[!is.na(circ_data$ctDNA.Stage.II.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.Risk, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.Risk, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.Risk=1 30 1 NA NA NA
ctDNA.Stage.II.Risk=2 2 0 NA NA NA
ctDNA.Stage.II.Risk=3 110 4 NA NA NA
ctDNA.Stage.II.Risk=4 8 3 NA 11.1 NAevent_summary <- circ_data %>%
group_by(ctDNA.Stage.II.Risk) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.Risk, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage II Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.Risk, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.Risk=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 11.0000 1.0000 0.9600 0.0392 0.7484 0.9943
ctDNA.Stage.II.Risk=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 1 0 1 0 NA NA
ctDNA.Stage.II.Risk=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 35.0000 4.0000 0.9465 0.0278 0.8549 0.9809
ctDNA.Stage.II.Risk=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 5.000 3.000 0.625 0.171 0.229 0.861 circ_data$ctDNA.Stage.II.Risk <- factor(circ_data$ctDNA.Stage.II.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxphf(surv_object ~ ctDNA.Stage.II.Risk, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Stage.II.Risk, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.Stage.II.RiskctDNA(+) & Low Risk 1.54746514 1.827452 4.6995424 0.03219659 88.15970 0.670679229 0.41281490
ctDNA.Stage.II.RiskctDNA(-) & High Risk -0.06180044 1.054866 0.9400705 0.17388788 9.38239 0.004245486 0.94804868
ctDNA.Stage.II.RiskctDNA(+) & High Risk 2.30998495 1.092244 10.0742730 1.65326183 104.09858 6.196660240 0.01279916
Likelihood ratio test=9.240477 on 3 df, p=0.02625872, n=150
Wald test = 9.971067 on 3 df, p = 0.01881368
Covariance-Matrix:
ctDNA.Stage.II.RiskctDNA(+) & Low Risk ctDNA.Stage.II.RiskctDNA(-) & High Risk ctDNA.Stage.II.RiskctDNA(+) & High Risk
ctDNA.Stage.II.RiskctDNA(+) & Low Risk 3.3395808 0.8333478 0.8321149
ctDNA.Stage.II.RiskctDNA(-) & High Risk 0.8333478 1.1127416 0.8335422
ctDNA.Stage.II.RiskctDNA(+) & High Risk 0.8321149 0.8335422 1.1929966#DFS by ctDNA at the MRD Window - Stage III & Risk Groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.III.Risk = case_when(
ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.III.Risk, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.III.Risk=1 103 10 NA NA NA
ctDNA.Stage.III.Risk=2 13 9 12.9 6.14 NA
ctDNA.Stage.III.Risk=3 67 14 NA NA NA
ctDNA.Stage.III.Risk=4 38 24 9.0 6.01 NAcirc_data <- circ_data[!is.na(circ_data$ctDNA.Stage.III.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.III.Risk, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.III.Risk=1 103 10 NA NA NA
ctDNA.Stage.III.Risk=2 13 9 12.9 6.14 NA
ctDNA.Stage.III.Risk=3 67 14 NA NA NA
ctDNA.Stage.III.Risk=4 38 24 9.0 6.01 NAevent_summary <- circ_data %>%
group_by(ctDNA.Stage.III.Risk) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(18))Call: survfit(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.III.Risk=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 57.0000 8.0000 0.9095 0.0312 0.8248 0.9543
ctDNA.Stage.III.Risk=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 2.0000 8.0000 0.3077 0.1417 0.0793 0.5780
ctDNA.Stage.III.Risk=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 38.0000 11.0000 0.8141 0.0512 0.6878 0.8932
ctDNA.Stage.III.Risk=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 8.0000 24.0000 0.3038 0.0812 0.1574 0.4641 circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data)
n= 221, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.III.RiskctDNA(+) & Low Risk 2.4892 12.0518 0.4642 5.362 8.23e-08 ***
ctDNA.Stage.III.RiskctDNA(-) & High Risk 0.7524 2.1221 0.4141 1.817 0.0692 .
ctDNA.Stage.III.RiskctDNA(+) & High Risk 2.3908 10.9222 0.3791 6.307 2.85e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.III.RiskctDNA(+) & Low Risk 12.052 0.08297 4.8517 29.937
ctDNA.Stage.III.RiskctDNA(-) & High Risk 2.122 0.47123 0.9425 4.778
ctDNA.Stage.III.RiskctDNA(+) & High Risk 10.922 0.09156 5.1955 22.961
Concordance= 0.757 (se = 0.033 )
Likelihood ratio test= 57.24 on 3 df, p=2e-12
Wald test = 55.9 on 3 df, p=4e-12
Score (logrank) test = 79.25 on 3 df, p=<2e-16rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.III.Risk = case_when(
ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.III.Risk, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.III.Risk=1 103 10 NA NA NA
ctDNA.Stage.III.Risk=2 13 9 12.9 6.14 NA
ctDNA.Stage.III.Risk=3 67 14 NA NA NA
ctDNA.Stage.III.Risk=4 38 24 9.0 6.01 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(18))Call: survfit(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.III.Risk=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 57.0000 8.0000 0.9095 0.0312 0.8248 0.9543
ctDNA.Stage.III.Risk=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 2.0000 8.0000 0.3077 0.1417 0.0793 0.5780
ctDNA.Stage.III.Risk=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 38.0000 11.0000 0.8141 0.0512 0.6878 0.8932
ctDNA.Stage.III.Risk=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 8.0000 24.0000 0.3038 0.0812 0.1574 0.4641 circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data)
n= 221, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.III.Risk4 -0.09842 0.90627 0.39258 -0.251 0.802
ctDNA.Stage.III.Risk1 -2.48922 0.08297 0.46424 -5.362 8.23e-08 ***
ctDNA.Stage.III.Risk3 -1.73680 0.17608 0.43279 -4.013 5.99e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.III.Risk4 0.90627 1.103 0.41985 1.9562
ctDNA.Stage.III.Risk1 0.08297 12.052 0.03340 0.2061
ctDNA.Stage.III.Risk3 0.17608 5.679 0.07539 0.4113
Concordance= 0.757 (se = 0.033 )
Likelihood ratio test= 57.24 on 3 df, p=2e-12
Wald test = 55.9 on 3 df, p=4e-12
Score (logrank) test = 79.25 on 3 df, p=<2e-16#OS by ctDNA at the MRD Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 334 8 NA NA NA
ctDNA.MRD=POSITIVE 71 7 40.4 40.4 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
2.40e+01 1.52e+02 7.00e+00 9.75e-01 9.49e-03 9.48e-01 9.88e-01
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 30.0000 5.0000 0.9064 0.0408 0.7856 0.9607 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 405, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.551 4.717 0.519 2.988 0.0028 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 4.717 0.212 1.705 13.04
Concordance= 0.633 (se = 0.068 )
Likelihood ratio test= 7.93 on 1 df, p=0.005
Wald test = 8.93 on 1 df, p=0.003
Score (logrank) test = 10.85 on 1 df, p=0.001cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.72 (1.71-13.04); p = 0.003"#DFS by ctDNA at the Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 540 20 NA NA NA
ctDNA.Surveillance=POSITIVE 83 54 16.2 13.7 28.4event_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
2.40e+01 2.83e+02 1.60e+01 9.64e-01 9.06e-03 9.41e-01 9.78e-01
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 21.0000 44.0000 0.4087 0.0597 0.2916 0.5222 circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 623, number of events= 74
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.2926 26.9135 0.2645 12.45 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 26.91 0.03716 16.03 45.19
Concordance= 0.817 (se = 0.026 )
Likelihood ratio test= 173.2 on 1 df, p=<2e-16
Wald test = 155 on 1 df, p=<2e-16
Score (logrank) test = 344 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 26.91 (16.03-45.19); p = 0"#DFS by ctDNA at the Surveillance Window - Stages II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 198 6 NA NA NA
ctDNA.Surveillance=POSITIVE 16 9 21.2 11.1 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 99.000 5.000 0.967 0.015 0.920 0.986
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 3.000 7.000 0.480 0.149 0.187 0.725 circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 214, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.4093 30.2449 0.5373 6.345 2.23e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 30.24 0.03306 10.55 86.7
Concordance= 0.782 (se = 0.063 )
Likelihood ratio test= 34.66 on 1 df, p=4e-09
Wald test = 40.26 on 1 df, p=2e-10
Score (logrank) test = 94.31 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 30.24 (10.55-86.7); p = 0"#DFS by ctDNA at the Surveillance Window - Stages III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 306 13 NA NA NA
ctDNA.Surveillance=POSITIVE 64 45 15.8 13.1 28.4event_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 163.0000 10.0000 0.9610 0.0123 0.9281 0.9790
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 18.0000 37.0000 0.3800 0.0646 0.2553 0.5037 circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 370, number of events= 58
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.2208 25.0476 0.3173 10.15 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 25.05 0.03992 13.45 46.65
Concordance= 0.827 (se = 0.027 )
Likelihood ratio test= 129.4 on 1 df, p=<2e-16
Wald test = 103 on 1 df, p=<2e-16
Score (logrank) test = 222.1 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 25.05 (13.45-46.65); p = 0"#OS by ctDNA at the Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 540 4 NA NA NA
ctDNA.Surveillance=POSITIVE 83 4 NA NA NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
2.40e+01 3.32e+02 3.00e+00 9.94e-01 3.65e-03 9.80e-01 9.98e-01
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 41.0000 3.0000 0.9594 0.0231 0.8785 0.9868 circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 623, number of events= 8
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 1.9570 7.0779 0.7088 2.761 0.00576 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 7.078 0.1413 1.764 28.39
Concordance= 0.687 (se = 0.094 )
Likelihood ratio test= 6.66 on 1 df, p=0.01
Wald test = 7.62 on 1 df, p=0.006
Score (logrank) test = 10.36 on 1 df, p=0.001cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.08 (1.76-28.39); p = 0.006"#Time-dependent analysis in surveillance window - all stages
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)Warning in Surv(tstart, tstop, dfs_event) :
Stop time must be > start time, NA createdsummary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 3456, number of events= 95
(1211 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 3.6369 37.9742 0.2235 16.27 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 37.97 0.02633 24.51 58.84
Concordance= 0.816 (se = 0.024 )
Likelihood ratio test= 256.4 on 1 df, p=<2e-16
Wald test = 264.9 on 1 df, p=<2e-16
Score (logrank) test = 679.7 on 1 df, p=<2e-16#Time-dependent analysis in surveillance window - all stages ACT-treated
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)Warning in Surv(tstart, tstop, dfs_event) :
Stop time must be > start time, NA createdsummary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 2178, number of events= 69
(1014 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 3.5174 33.6971 0.2601 13.52 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 33.7 0.02968 20.24 56.11
Concordance= 0.807 (se = 0.029 )
Likelihood ratio test= 175.9 on 1 df, p=<2e-16
Wald test = 182.8 on 1 df, p=<2e-16
Score (logrank) test = 443.9 on 1 df, p=<2e-16#Time-dependent analysis in surveillance window - all stages Non-ACT-treated
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="FALSE",]
dt_final <- dt_final[dt_final$tstart!="",]
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)Warning in Surv(tstart, tstop, dfs_event) :
Stop time must be > start time, NA createdsummary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 1278, number of events= 26
(197 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 3.8078 45.0513 0.4362 8.73 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 45.05 0.0222 19.16 105.9
Concordance= 0.835 (se = 0.043 )
Likelihood ratio test= 76.31 on 1 df, p=<2e-16
Wald test = 76.21 on 1 df, p=<2e-16
Score (logrank) test = 214.3 on 1 df, p=<2e-16#DFS by ACT treatment in MRD negative - High Risk Stage II or Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 93 5 NA NA NA
ACT=TRUE 187 23 NA NA NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 30.0000 5.0000 0.9186 0.0372 0.8054 0.9673
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 68.000 19.000 0.878 0.027 0.813 0.921 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 280, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE -0.7813 0.4578 0.4935 -1.583 0.113
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 0.4578 2.184 0.174 1.204
Concordance= 0.571 (se = 0.04 )
Likelihood ratio test= 2.93 on 1 df, p=0.09
Wald test = 2.51 on 1 df, p=0.1
Score (logrank) test = 2.64 on 1 df, p=0.1cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 0.46 (0.17-1.2); p = 0.113"#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + MSI + Stage, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Age.Group + Gender + MSI +
Stage, data = circ_data)
n= 280, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE -0.1808 0.8346 0.6123 -0.295 0.7678
Age.Group≥70 0.3185 1.3750 0.4494 0.709 0.4785
GenderMale 0.5723 1.7724 0.4009 1.428 0.1534
MSIMSI-High 0.2271 1.2550 0.5780 0.393 0.6944
StageIII 1.3172 3.7330 0.6395 2.060 0.0394 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 0.8346 1.1982 0.2513 2.772
Age.Group≥70 1.3750 0.7273 0.5699 3.318
GenderMale 1.7724 0.5642 0.8078 3.889
MSIMSI-High 1.2550 0.7968 0.4042 3.896
StageIII 3.7330 0.2679 1.0658 13.075
Concordance= 0.668 (se = 0.055 )
Likelihood ratio test= 10.58 on 5 df, p=0.06
Wald test = 8.62 on 5 df, p=0.1
Score (logrank) test = 9.59 on 5 df, p=0.09#DFS by ACT treatment in MRD positive - High Risk Stage II or Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 10 9 2.41 1.22 NA
ACT=TRUE 51 29 13.38 10.19 NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 9.00000 0.10000 0.09487 0.00572 0.35813
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 11.0000 29.0000 0.3632 0.0744 0.2218 0.5060 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 61, number of events= 38
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.3968 4.0422 0.3909 3.573 0.000353 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 4.042 0.2474 1.879 8.697
Concordance= 0.613 (se = 0.036 )
Likelihood ratio test= 9.94 on 1 df, p=0.002
Wald test = 12.77 on 1 df, p=4e-04
Score (logrank) test = 14.86 on 1 df, p=1e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.04 (1.88-8.7); p = 0"#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + Stage, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Age.Group + Gender + Stage,
data = circ_data)
n= 61, number of events= 38
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.9310 6.8967 0.4580 4.216 2.48e-05 ***
Age.Group≥70 0.5599 1.7505 0.3603 1.554 0.1202
GenderMale 0.2086 1.2320 0.3471 0.601 0.5478
StageIII 1.3374 3.8093 0.5942 2.251 0.0244 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 6.897 0.1450 2.8106 16.923
Age.Group≥70 1.750 0.5713 0.8640 3.547
GenderMale 1.232 0.8117 0.6239 2.432
StageIII 3.809 0.2625 1.1887 12.208
Concordance= 0.669 (se = 0.044 )
Likelihood ratio test= 18.74 on 4 df, p=9e-04
Wald test = 20.61 on 4 df, p=4e-04
Score (logrank) test = 22.94 on 4 df, p=1e-04#DFS by ctDNA at the MRD Window & ACT - MSS Stable all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.ACT = case_when(
ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 169 19 NA NA NA
ctDNA.ACT=2 48 27 13.07 9.69 NA
ctDNA.ACT=3 106 6 NA NA NA
ctDNA.ACT=4 3 2 7.42 5.29 NAevent_summary <- circ_data %>%
group_by(ctDNA.ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.ACT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 61.000 15.000 0.892 0.027 0.825 0.934
ctDNA.ACT=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 9.0000 27.0000 0.3640 0.0773 0.2174 0.5120
ctDNA.ACT=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 37.000 6.000 0.921 0.032 0.829 0.965
ctDNA.ACT=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 2.00000 0.33333 0.27217 0.00896 0.77415 circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.ACT, data = circ_data)
n= 326, number of events= 54
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.ACTctDNA(+) & ACT 2.0237 7.5661 0.3021 6.700 2.09e-11 ***
ctDNA.ACTctDNA(-) & No ACT -0.6820 0.5056 0.4683 -1.456 0.14536
ctDNA.ACTctDNA(+) & No ACT 2.1276 8.3950 0.7463 2.851 0.00436 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.ACTctDNA(+) & ACT 7.5661 0.1322 4.1856 13.677
ctDNA.ACTctDNA(-) & No ACT 0.5056 1.9778 0.2019 1.266
ctDNA.ACTctDNA(+) & No ACT 8.3950 0.1191 1.9443 36.247
Concordance= 0.759 (se = 0.034 )
Likelihood ratio test= 61 on 3 df, p=4e-13
Wald test = 65.55 on 3 df, p=4e-14
Score (logrank) test = 98.87 on 3 df, p=<2e-16rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.ACT = case_when(
ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 169 19 NA NA NA
ctDNA.ACT=2 48 27 13.07 9.69 NA
ctDNA.ACT=3 106 6 NA NA NA
ctDNA.ACT=4 3 2 7.42 5.29 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.ACT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 61.000 15.000 0.892 0.027 0.825 0.934
ctDNA.ACT=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 9.0000 27.0000 0.3640 0.0773 0.2174 0.5120
ctDNA.ACT=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 37.000 6.000 0.921 0.032 0.829 0.965
ctDNA.ACT=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 2.00000 0.33333 0.27217 0.00896 0.77415 circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & No ACT","ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.ACT, data = circ_data)
n= 326, number of events= 54
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.ACTctDNA(-) & ACT 0.6820 1.9778 0.4683 1.456 0.145361
ctDNA.ACTctDNA(+) & ACT 2.7056 14.9638 0.4533 5.969 2.39e-09 ***
ctDNA.ACTctDNA(+) & No ACT 2.8096 16.6032 0.8192 3.430 0.000605 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.ACTctDNA(-) & ACT 1.978 0.50562 0.7898 4.953
ctDNA.ACTctDNA(+) & ACT 14.964 0.06683 6.1544 36.383
ctDNA.ACTctDNA(+) & No ACT 16.603 0.06023 3.3331 82.704
Concordance= 0.759 (se = 0.034 )
Likelihood ratio test= 61 on 3 df, p=4e-13
Wald test = 65.55 on 3 df, p=4e-14
Score (logrank) test = 98.87 on 3 df, p=<2e-16#DFS by ctDNA at the MRD Window & ACT - MSI High all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.ACT = case_when(
ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 19 4 NA NA NA
ctDNA.ACT=2 5 2 NA 3.09 NA
ctDNA.ACT=3 34 0 NA NA NA
ctDNA.ACT=4 6 5 1.91 1.41 NAevent_summary <- circ_data %>%
group_by(ctDNA.ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(6, 24))Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.ACT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 15 3 0.839 0.0854 0.579 0.945
24 3 1 0.774 0.1003 0.502 0.910
ctDNA.ACT=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 3 2 0.6 0.219 0.126 0.882
24 2 0 0.6 0.219 0.126 0.882
ctDNA.ACT=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 31 0 1 0 NA NA
24 9 0 1 0 NA NA
ctDNA.ACT=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.00000 1.00000 5.00000 0.16667 0.15215 0.00772 0.51680 circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500, maxstep = 1) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.ACT, data = circ_data, maxit = 500,
maxstep = 1)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.ACTctDNA(+) & ACT 2.813027 1.622557 16.66027 1.778749 2207.805 6.609234 1.014513e-02
ctDNA.ACTctDNA(-) & No ACT 3.712830 1.686348 40.96957 3.329543 5652.756 8.497597 3.556157e-03
ctDNA.ACTctDNA(+) & No ACT 4.942760 1.627985 140.15657 14.930527 18714.894 24.151928 8.902699e-07
Likelihood ratio test=26.64366 on 3 df, p=6.992091e-06, n=64
Wald test = 14.30441 on 3 df, p = 0.002518759
Covariance-Matrix:
ctDNA.ACTctDNA(+) & ACT ctDNA.ACTctDNA(-) & No ACT ctDNA.ACTctDNA(+) & No ACT
ctDNA.ACTctDNA(+) & ACT 2.632691 2.370004 2.371709
ctDNA.ACTctDNA(-) & No ACT 2.370004 2.843769 2.373722
ctDNA.ACTctDNA(+) & No ACT 2.371709 2.373722 2.650336rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.ACT = case_when(
ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 19 4 NA NA NA
ctDNA.ACT=2 5 2 NA 3.09 NA
ctDNA.ACT=3 34 0 NA NA NA
ctDNA.ACT=4 6 5 1.91 1.41 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(6))Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.ACT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.0000 15.0000 3.0000 0.8388 0.0854 0.5788 0.9451
ctDNA.ACT=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.000 3.000 2.000 0.600 0.219 0.126 0.882
ctDNA.ACT=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 31 0 1 0 NA NA
ctDNA.ACT=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.00000 1.00000 5.00000 0.16667 0.15215 0.00772 0.51680 circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("2","4","1","3"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.ACT, data = circ_data, maxit = 500)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.ACT4 1.2299304 0.8640952 3.42099155 0.771944824 20.3070261 2.600353 0.106839899
ctDNA.ACT1 -0.8998028 0.8581680 0.40664983 0.090118999 2.3336235 1.163885 0.280661276
ctDNA.ACT3 -3.7128298 1.6863479 0.02440835 0.000176905 0.3003415 8.497597 0.003556157
Likelihood ratio test=26.64366 on 3 df, p=6.992091e-06, n=64
Wald test = 14.30441 on 3 df, p = 0.002518759
Covariance-Matrix:
ctDNA.ACT4 ctDNA.ACT1 ctDNA.ACT3
ctDNA.ACT4 0.7466605 0.4717519 0.4700468
ctDNA.ACT1 0.4717519 0.7364524 0.4737650
ctDNA.ACT3 0.4700468 0.4737650 2.8437691#DFS by ctDNA Dynamics from MRD to Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
)) %>%
filter(!is.na(ctDNA.Dynamics))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 263 10 NA NA NA
ctDNA.Dynamics=2 16 1 NA NA NA
ctDNA.Dynamics=3 25 11 28.4 15.77 NA
ctDNA.Dynamics=4 20 15 10.6 8.05 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 93.000 8.000 0.961 0.014 0.922 0.981
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 8.0000 1.0000 0.9000 0.0949 0.4730 0.9853
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 9.000 0.519 0.125 0.260 0.727
ctDNA.Dynamics=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 15.0000 0.2400 0.0980 0.0821 0.4428 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 324, number of events= 37
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Dynamics2 0.5229 1.6869 1.0493 0.498 0.618
ctDNA.Dynamics3 2.6136 13.6480 0.4383 5.963 2.48e-09 ***
ctDNA.Dynamics4 3.5381 34.4018 0.4141 8.545 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics2 1.687 0.59281 0.2157 13.19
ctDNA.Dynamics3 13.648 0.07327 5.7804 32.22
ctDNA.Dynamics4 34.402 0.02907 15.2803 77.45
Concordance= 0.822 (se = 0.04 )
Likelihood ratio test= 79.02 on 3 df, p=<2e-16
Wald test = 77.71 on 3 df, p=<2e-16
Score (logrank) test = 168.8 on 3 df, p=<2e-16rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
)) %>%
filter(!is.na(ctDNA.Dynamics))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 263 10 NA NA NA
ctDNA.Dynamics=2 16 1 NA NA NA
ctDNA.Dynamics=3 25 11 28.4 15.77 NA
ctDNA.Dynamics=4 20 15 10.6 8.05 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 93.000 8.000 0.961 0.014 0.922 0.981
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 8.0000 1.0000 0.9000 0.0949 0.4730 0.9853
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 9.000 0.519 0.125 0.260 0.727
ctDNA.Dynamics=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 15.0000 0.2400 0.0980 0.0821 0.4428 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 324, number of events= 37
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Dynamics4 0.92452 2.52065 0.39952 2.314 0.0207 *
ctDNA.Dynamics1 -2.61359 0.07327 0.43834 -5.963 2.48e-09 ***
ctDNA.Dynamics2 -2.09071 0.12360 1.04642 -1.998 0.0457 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics4 2.52065 0.3967 1.15197 5.515
ctDNA.Dynamics1 0.07327 13.6480 0.03103 0.173
ctDNA.Dynamics2 0.12360 8.0907 0.01590 0.961
Concordance= 0.822 (se = 0.04 )
Likelihood ratio test= 79.02 on 3 df, p=<2e-16
Wald test = 77.71 on 3 df, p=<2e-16
Score (logrank) test = 168.8 on 3 df, p=<2e-16#DFS by ctDNA Dynamics from MRD to Surveillance Window - High Risk Stage II or Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
)) %>%
filter(!is.na(ctDNA.Dynamics))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 224 10 NA NA NA
ctDNA.Dynamics=2 14 1 NA NA NA
ctDNA.Dynamics=3 22 10 28.4 16.03 NA
ctDNA.Dynamics=4 19 15 10.1 8.05 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 77.0000 8.0000 0.9539 0.0166 0.9073 0.9774
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 7.000 1.000 0.889 0.105 0.433 0.984
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 8.000 0.542 0.128 0.272 0.750
ctDNA.Dynamics=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 15.0000 0.1974 0.0948 0.0551 0.4032 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 279, number of events= 36
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Dynamics2 0.4503 1.5688 1.0494 0.429 0.668
ctDNA.Dynamics3 2.4212 11.2599 0.4483 5.401 6.64e-08 ***
ctDNA.Dynamics4 3.4366 31.0803 0.4147 8.286 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics2 1.569 0.63743 0.2006 12.27
ctDNA.Dynamics3 11.260 0.08881 4.6765 27.11
ctDNA.Dynamics4 31.080 0.03217 13.7872 70.06
Concordance= 0.816 (se = 0.04 )
Likelihood ratio test= 72.14 on 3 df, p=1e-15
Wald test = 72.35 on 3 df, p=1e-15
Score (logrank) test = 150.9 on 3 df, p=<2e-16rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
)) %>%
filter(!is.na(ctDNA.Dynamics))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 224 10 NA NA NA
ctDNA.Dynamics=2 14 1 NA NA NA
ctDNA.Dynamics=3 22 10 28.4 16.03 NA
ctDNA.Dynamics=4 19 15 10.1 8.05 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 77.0000 8.0000 0.9539 0.0166 0.9073 0.9774
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 7.000 1.000 0.889 0.105 0.433 0.984
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 8.000 0.542 0.128 0.272 0.750
ctDNA.Dynamics=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 15.0000 0.1974 0.0948 0.0551 0.4032 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 279, number of events= 36
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Dynamics4 1.01533 2.76027 0.41237 2.462 0.0138 *
ctDNA.Dynamics1 -2.42124 0.08881 0.44832 -5.401 6.64e-08 ***
ctDNA.Dynamics2 -1.97094 0.13933 1.05059 -1.876 0.0607 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics4 2.76027 0.3623 1.23012 6.1938
ctDNA.Dynamics1 0.08881 11.2599 0.03689 0.2138
ctDNA.Dynamics2 0.13933 7.1774 0.01777 1.0922
Concordance= 0.816 (se = 0.04 )
Likelihood ratio test= 72.14 on 3 df, p=1e-15
Wald test = 72.35 on 3 df, p=1e-15
Score (logrank) test = 150.9 on 3 df, p=<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